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This Guidance demonstrates how to implement an automated machine learning (ML) pipeline to gain real-time player insights, powered by artificial intelligence (AI), enabling studios to better understand player behavior and improve the overall game experience. Game studios can leverage this low-code solution to quickly build, train, and deploy high-quality models that predict player behavior using their own gameplay data. Operators simply upload player data to Amazon Simple Storage Service (Amazon S3), which invokes an end-to-end workflow to extract insights, select algorithms, tune hyperparameters, evaluate models, and deploy the best performing model to a prediction API. This automated process requires no manual machine learning tasks while delivering real-time predictions that give studios valuable insights into individual player retention, engagement, and monetization to inform data-driven decisions that improve gameplay.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
Users capture player event data from their game.
Step 2
Tabular player data is uploaded to an Amazon Simple Storage Service (Amazon S3) bucket.
Step 3
The tabular data upload event invokes Amazon SageMaker pipelines.
Step 4
The Preprocessing step runs a SageMaker processing job to split the CSV data into training and validation datasets.
Step 5
The automatic machine learning (AutoML) step creates a SageMaker AutoML job to automatically train a machine learning (ML) model.
Step 6
The trained model artifacts are stored in an Amazon S3 bucket.
Step 7
The Evaluation step runs a SageMaker processing job to compare the performance of the trained ML model against the validation dataset.
Step 8
The trained model is stored in the SageMaker Model Registry.
Step 9
The registered ML model is deployed for production use.
Step 10
The registered ML model is hosted as a model endpoint using SageMaker.
Step 11
Game clients make inference requests to the hosted model to derive player insights and predict in-game player behavior.
Step 1
Users capture player event data from their game.
Step 2
Tabular player data is uploaded to an Amazon Simple Storage Service (Amazon S3) bucket.
Step 3
The tabular data upload event invokes Amazon SageMaker pipelines.
Step 4
The Preprocessing step runs a SageMaker processing job to split the CSV data into training and validation datasets.
Step 5
The automatic machine learning (AutoML) step creates a SageMaker AutoML job to automatically train a machine learning (ML) model.
Step 6
The trained model artifacts are stored in an Amazon S3 bucket.
Step 7
The Evaluation step runs a SageMaker processing job to compare the performance of the trained ML model against the validation dataset.
Step 8
The trained model is stored in the SageMaker Model Registry.
Step 9
The registered ML model is deployed for production use.
Step 10
The registered ML model is hosted as a model endpoint using SageMaker.
Step 11
Game clients make inference requests to the hosted model to derive player insights and predict in-game player behavior.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
This Guidance employs AWS X-Ray and SageMaker Autopilot to enable observability and model experiment tracking. X-Ray traces AWS Lambda functions and logs the interactions with other AWS services, allowing you to visualize components to identify bottlenecks and troubleshoot errors. SageMaker Autopilot logs model training runs and candidate performance. You can view evaluation metrics and charts to understand how the best model was selected based on the data. Together, these capabilities allow transparency into system and ML model performance over time, facilitating quick diagnosis of issues and informed model and architecture optimization decisions, critical for cost-efficient operations.
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SecurityRead the Security whitepaper
Amazon S3 with AWS Key Management Service (AWS KMS) ensures that data is encrypted at rest. Specifically, all ML training data and the trained ML models are encrypted using keys from AWS KMS. SageMaker endpoints encrypts all communication in transit. Together, these capabilities allow secure storage and communication of sensitive data like player information and ML models.
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ReliabilityRead the Reliability whitepaper
SageMaker hosted endpoints automatically distribute hosted models across multiple AWS Availability Zones (AZs). This allows ML model inference requests to sustain AZ failures. SageMaker hosted endpoints effectively load balance inference requests from game clients and servers across multiple copies of the ML models. By spreading requests across AZs, this Guidance ensures continued service uptime and consistent real-time player predictions.
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Performance EfficiencyRead the Performance Efficiency whitepaper
This Guidance optimizes performance efficiency by leveraging SageMaker, which manages an elastic fleet of ML instances that scale up and down based on demand. By load balancing requests across a dynamic number of models, inference latency is reduced significantly compared to a single instance. Rather than overwhelming a single model and causing queueing delays, inferences are handled in parallel to improve throughput. The low latency and scalable capacity ensure real-time predictions that instantly inform game adaptations even under heavy loads, without performance degradation that could negatively impact the player experience.
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Cost OptimizationRead the Cost Optimization whitepaper
By leveraging serverless and scalable inferences, this Guidance provides player predictions in a cost-efficient manner. Specifically, SageMaker automatically scales inference resources up and down to match real-time request demand. Serverless hosting means you pay only for the duration of each inference request. The ML models are used interchangeably for real-time and serverless hosting. This allows you to switch between modes to best align with the current scale and cost requirements. Games with volatile player activity can rely on inference at scale when hot while recouping costs when cooler. The optimization saves significantly on unused instances while still powering core player insights.
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SustainabilityRead the Sustainability whitepaper
By eliminating resource idle time and right-sizing storage, this Guidance minimizes your carbon footprint to drive sustainability. To start, by using SageMaker serverless technologies for data processing and ML training jobs, you reduce any idle compute resources. And Amazon S3 implements lifecycle policies to archive infrequent access training data into energy-efficient storage tiers. You can select the appropriate Amazon S3 storage class to reduce your carbon impact based on access patterns. An AutoML serverless architecture also limits infrastructure maintenance and unnecessary provisioning. Together, these capabilities minimize resource waste, both compute and storage, to reduce energy demands and environmental impact.
Implementation Resources
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.